Our approach

We are a structure of qualified professionals’ experts. We help to create high quality evidence. We are also focused on improving data quality, conducting rigorous quantitative and qualitative data analysis, and improving data visualization to help our partners and customers to learn from their activities.

As a social enterprise based on innovation, under experimental and evidence-based form, we initiate socio-economic projects (action research) in the fields of inclusive finance, social inclusion & entrepreneurship, education, and public health with our profits from services consultancy.

We understand and recognize that rigour methodology is important not only for ensuring good data, but also to create high quality evidence. Therefore:

1

We implement the highest quality sampling methods in all data collection, research and/or evaluation.

2

Our enumerators are experienced and specific: each project is unique at IES.

3

Our data collection teams are context and gender sensitive; conduct face-to-face interviews with interviewees, in needed, conduct data collection in remotest locations.

4

Our data collection teams use test cutting-edge tools in the field (qualitative and/or quantitative methods).

5

Our experts conduct personally in-field training sessions before data collection team’s deployment (data collection tools testing, ethical counsels, rigorous and standardized instruction in research methodology and practice).

6

Our data collection is based on the new technology: we use of android tablets with the most up to date software, we receive data in real time at our offices, our field team receive timely feed-back from our data analysis and verification team.

7

We provide independent, timely and contextualized third-party monitoring and operational support services to many of our clients.

8

As an innovative company, familiar with the context and well anchored in the most remote areas of the DRC, we combine several data collection approaches according to the needs, urgency and context while ensuring quality: remote and/or in-person data collection.

9

Collected data are verified before their process.

10

We use adequate data analysis software for geo-location data (Arcview, Q-GIS, etc.) for quantitative data (Python, R, Stata, SPSS, E-views, etc) and qualitative data (Nvivo, MAXQDA, ATLAS, etc).

12

Before reporting, we usually organize validation sessions of the data collected and analyzed by the participants and other stakeholders for accountability and data certification.

13

Our data reporting team is professional, multidisciplinary, multicultural, and multilingual (French, English, Spanish, and national/local languages).